Species assemblage networks identify regional connectivity pathways among hydrothermal vents in the Northwest Pacific

Abstract The distribution of species among spatially isolated habitat patches supports regional biodiversity and stability, so understanding the underlying processes and structure is a key target of conservation. Although multivariate statistics can infer the connectivity processes driving species distribution, such as dispersal and habitat suitability, they rarely explore the structure. Methods from graph theory, applied to distribution data, give insights into both connectivity pathways and processes by intuitively formatting the data as a network of habitat patches. We apply these methods to empirical data from the hydrothermal vent habitats of the Northwest Pacific. Hydrothermal vents are “oases” of biological productivity and endemicity on the seafloor that are imminently threatened by anthropogenic disturbances with unknown consequences to biodiversity. Here, we describe the structure of species assemblage networks at hydrothermal vents, how local and regional parameters affect their structure, and the implications for conservation. Two complementary networks were formed from an extensive species assemblage dataset: a similarity network of vent site nodes linked by weighted edges based on their pairwise assemblage similarity and a bipartite network of species nodes linked to vent site nodes at which they are present. Using these networks, we assessed the role of individual vent sites in maintaining network connectivity and identified biogeographic sub‐regions. The three sub‐regions and two outlying sites are separated by their spatial arrangement and local environmental filters. Both networks detected vent sites that play a disproportionately important role in regional pathways, while the bipartite network also identified key vent sites maintaining the distinct species assemblages of their sub‐regions. These regional connectivity pathways provide insights into historical colonization routes, while sub‐regional connectivity pathways are of value when selecting sites for conservation and/or estimating the multivent impacts from proposed deep‐sea mining.


| INTRODUC TI ON
Conservation efforts aim to slow the global degradation of biodiversity Meine et al., 2006 as well as ecosystem functions and services (Nicholson et al., 2009). These features of any ecosystem are supported by ecological connectivity, which is the flow of organisms, energy, and materials across suitable habitat patches (Crooks & Sanjayan, 2006;Correa Ayram et al., 2016). Structural or functional isolation of habitat patches by natural or anthropogenic disturbances may limit the capacity of an ecosystem to maintain processes that are valued within conservation objectives (Rudnick et al., 2012) by disrupting landscape connectivity. If the dispersal of individuals is impeded, species may become more vulnerable to global extinction by reducing their abilities to shift their ranges in response to climate change or support local recovery following disturbance events (reviewed by Jones et al., 2007). For these reasons, maintaining the structure of landscape connectivity is a global priority (IUCN, 2017).
Hydrothermal vents are often described as "oases" of high biomass in the deep (Laubier, 1993), as local chemoautotrophy not only supports higher densities of benthic species within the vent ecosystem but also contributes to the surrounding nonvent ecosystems (reviewed in Levin et al., 2016). Vents are also home to seafloor massive sulfide deposits, a primary target for deep-sea mining in recent years (Van Dover et al., 2018). Where these ecosystems coincide with mining interests, regional diversity may become vulnerable (Van Dover et al., 2018) and a global priority for protection (Thomas, Molloy, et al., 2021). In the Northwest Pacific, where the first test mining of hydrothermal vents occurred in the Okinawa Trough Okamoto et al., 2019, studies have assessed the impact on the local vent community and surrounding habitats at the site of the proposed mining Nakajima et al., 2015). Although over three-fourths of vent-endemic species in the Northwest Pacific are considered threatened by deepsea mining , very few studies have attempted to assess the effect this proposed mining will have on the regional vent communities (Suzuki et al., 2018). Here we use empirical observations to describe the structure of connectivity among spatially isolated vent communities to investigate the regional impacts of deep-sea mining.
Connectivity among vent communities is facilitated by the dispersal of planktonic larvae Adams et al., 2012. Although it is possible to quantify dispersal probabilities using oceanographic simulations (Mitarai et al., 2016), dispersal is just one of several processes required for demographic connectivity among discrete communities.
Reproduction, larval dispersal, settlement, and maturation are the sequential steps necessary to maintain demographic connectivity among sites (Kritzer & Sale, 2004). Therefore, connectivity is controlled by a combination of local and regional processes such as dispersal probability, habitat suitability, and biological interactions (reviewed in Pineda et al., 2007). We investigate the drivers of diversity by formatting species' distributions of species as a network and then test how some local and regional drivers of diversity can explain the structure of this network. Such "similarity networks" have seldom been applied to empirical observations of linked but spatially distinct communities (metacommunities) (Borthagaray et al., 2015), despite a growing body of literature that has theoretically demonstrated the value of metacommunity networks to the study of biodiversity in general Keitt et al., 1997;Economo & Keitt, 2010;Suzuki & Economo, 2021).
Previous studies have also presented hydrothermal vents as networks and applied methods from graph theory to detect biogeographic regions and infer historical connectivity pathways at the global scale (Kiel, 2016;Moalic et al., 2012). Here, we focus on similar pathways among hydrothermal vent sites of the Northwest Pacific at an intra-regional scale more relevant to contemporary conservation. The Northwest Pacific is a distinct biogeographic region in terms of vent-endemic fauna Bachraty et al., 2009 and dispersal through oceanographic simulations (Mitarai et al., 2016). Within this region, the interactions between the Pacific Plate, the Philippine Plate, and the Eurasian Plate create active tectonic margins containing trenches, volcanic arcs, and back-arc spreading centres. Hydrothermal vents are present in the volcanic arcs and back arcs of the Izu-Bonin, Mariana, and Okinawa ( Figure 1). In these arc-back-arc basins, 78 known hydrothermal vent sites are recognized by the InterRidge database (Beaulieu & Szafrański, 2020). Although the Northwest Pacific is one of the better-surveyed regions in terms of hydrothermal vent biodiversity, few regions are considered to have been surveyed comprehensively (Thaler & Amon, 2019). By considering the vent sites of the Northwest Pacific as an interconnected network, we can apply structural analyses from graph theory to assess the roles individual vent sites play in sharing species across the region. Maintaining connectivity through shared species within a hydrothermal vent biogeographic region is a conservation priority due to its importance to biodiversity and its vulnerability to anthropogenic disturbances (Turner et al., 2019). We use empirical observations to investigate the processes that maintain connectivity as such an approach is a recognized knowledge gap (Amon et al., 2022;Van Dover, 2014

T A X O N O M Y C L A S S I F I C A T I O N
Biogeography, Community ecology, Conservation ecology, Ecosystem ecology, Population ecology, Spatial ecology . These studies have inferred connections using the number of shared species or β-diversity between sites and/or SIMPROF analysis Clarke et al., 2008 to detect significantly similar species assemblages among vent sites and to infer the biogeographic barriers that separate others. In this study, we combined and curated the occurrence data from these previous studies along with new occurrence data to create a comprehensive view of the regional species assemblages, with representation from the three major arc-back-arc systems of the region. For comparability, we first replicated the same clustering methods used in the aforementioned studies. We then expanded and improved upon the previous studies by applying methods from graph theory, which offers a distinct advantage over more classical pairwise analyses of connectivity (Proulx et al., 2005) and the detection of biogeographic barriers (Bloomfield et al., 2018). As the connectivity patterns of the regional networks are inferred from shared species, they provide insights into the geological and biogeographic history of the region and the ability to identify those vent sites with a key role in maintaining the diversity structure of the region.

| Species occurrence data
We assembled occurrence records of vent-associated benthic megafauna from a variety of published and new data (Table S1). The occurrence records were identified to the lowest taxonomic level with some published records being updated based on a review of recent taxonomic literature. Only species-level records were used in this study to ensure compatibility between the different data sources and because this is the required taxonomic resolution when studying processes at the metacommunity and sub-regional scale (Webb et al., 2003). Species names were checked against the World Register of Marine Species (WoRMS, http://www.marin espec ies. org/) to ensure up-to-date nomenclature. All occurrences were associated with a named vent site in the InterRidge database (Beaulieu & Szafrański, 2020) based on its geographic location or associated metadata to create a site-by-species matrix of 36 vent fields and 117 species (Table S2). Due to the remote nature of vent ecosystems and the difficulty in carrying out comprehensive surveys, this matrix is a "presence-only" dataset, as species absence from vent sites cannot be confirmed.

| Similarity network
We calculated the Sørensen's coefficient (Sorensen, 1948) from the site-by-species matrix to give a pairwise dissimilarity between all vent sites based on their species assemblages. The Sørensen's coefficient was used as it is applicable to presence-absence datasets, and gives extra weight to the shared presence (Legendre & Legendre, 2012). Furthermore, the Sørensen's coefficient was used to compare results with those of previous studies in this region Nakajima et al., 2014;Watanabe et al., 2019;, which also used this F I G U R E 1 The vent sites of (a) the Northwest Pacific, (b) Izu-Bonin, (c) Mariana, and (d) Okinawa were used in this study. The color of the vent sites represents the distinct sub-regions as detected from the modularity analysis (purple-Okinawa Trough (OT), orange-Minami-Ensei Knoll (MEK), red-Sumisu Caldera (SmsC), green-Izu-Bonin-Mariana Arc, blue-Mariana Trough (MT)). Lines connecting vent sites represent the pairwise similarity (Sorensen's coefficient) among species assemblages at vent sites. Red lines represent higher similarity while yellow lines represent lower similarity. coefficient. A subsequent SIMPROF analysis (Clarke et al., 2008) used 1000 permutations at 5% significance to hierarchically group vent sites into clusters (hereafter referred to as SIMPROF clusters to avoid confusion with the defined term "network cluster" used in graph theory) that had similar species assemblages.
Using the pairwise similarity (1-Sørensen's coefficient) between vent sites, we created a network of vent sites in the Northwest Pacific.
The similarity value was used as the weight of the edges that link the vent site nodes in this network, hereafter referred to as a "similarity network." The "percolation threshold" of the similarity network was calculated following the methods of Rozenfeld et al. (2008) using the "sidier" package in R (Muñoz-Pajares, 2013; R Core Team, 2021) and used to remove "weak" links. The relative importance of each vent site in maintaining a connected network was then evaluated based on their "betweenness centrality" (Freeman, 1977), the frequency they occur in the geodesic path between each pair of vents in the network. Betweenness centrality was calculated for every node in the similarity network after thresholding using the "igraph" package in R (Csardi & Nepusz, 2006).
We used variance partitioning to determine the contribution of environmental and spatial parameters to explain the β-diversity (Legendre & Legendre, 2012) represented by the edge weight between nodes in the species assemblage network. The spatial parameter in question was calculated using "distance-based Moran's Eigenvector Maps" (dbMEM) following the methods detailed in Legendre and Legendre (2012). These dbMEMs summarize the relative position of each site based on their geodesic distance from their neighboring sites. Two sets of dbMEM were created; the "fine-scale dbMEM" uses a threshold of geodesic distance lower than that required to connect sites within the Okinawa Trough to other sites in the Northwest Pacific, while the "broad-scale dbMEM" does consider this connectivity when calculating relative position. The environmental parameters tested were the depth and tectonic setting as recorded in the InterRidge database (Beaulieu & Szafrański, 2020) with some additional corrections ( Table 1). These local environmental variables were selected because they have been recorded for every vent site in the dataset and are indicative of the many processes that directly affect local habitat suitability (Giguère & Tunnicliffe, 2021;Mullineaux et al., 2018;Tunnicliffe et al., 1998). The variance partitioning analyses and the formation of the dbMEMs were carried out using the "vegan" package in R (Oksanen et al., 2019).

| Bipartite network
The second network was formed directly from the site-by-species matrix and is referred to as a bipartite network, after the two types of nodes it contains. The first node type, a species node, is linked to the second type, a vent site node, if the species was present at said vent site. The nodes in this network are linked by unweighted edges that only occur between nodes of a different type (i.e., species-vent site). This network approach has been implemented in various biogeographic studies (Carstensen et al., 2012;Dalsgaard et al., 2014;Kougioumoutzis et al., 2014Kougioumoutzis et al., , 2017 to detect barriers to "biogeographical connectivity." Following the methods of Carstensen et al. (2012), a simulated annealing approach was used to subdivide the regional bipartite network iteratively into groups until the grouping that maximizes the "Modularity" value of the network is identified.
The "Modularity" is a measure of the extent to which nodes have more links within their group than expected if the links are random (Guimerà & Nunes Amaral, 2005a, 2005b. For this analysis, we used the "rnetcarto" package in R (Doulcier & Stouffer, 2015;R Core Team, 2021). The resultant groups of highly linked nodes in the bipartite network are hereafter referred to as "modules." Redundancy analysis was used to detect the possible roles of known biogeographic barriers-depth, tectonic setting, and distance (broad-scale dbMEM)-on module membership, following the recommendations of Legendre and Legendre (2012). Additionally, a MANOVA analysis was carried out with the same formula to detect any significant variation of each explanatory variable and their interacting terms between the module groups.
Each node's role in connecting the bipartite network was assessed based on its within-module degree (z i ) and participation coefficient (Guimerà & Nunes Amaral, 2005a, 2005b. As the direct links to a vent site node come from the species it contains, the position of a vent site node in z i -P i space is indicative of its species richness, the regional distribution of those species, and the role the site itself plays in connecting spatially isolated species assemblages (Carstensen et al., 2012). Each vent node was assigned one of the universal cartographic roles defined by Guimerà and Nunes Amaral's (2005a).

| Similarity network
The similarity network shows three distinct groups in the form of qualitative network clusters (Newman, 2010) once the percolation threshold of 0.7 was applied. The edges that remain after this threshold and how they connect vent sites can be seen in a geographic ( Figure 1) or simplified layout ( Figure 2). The simplified layout positions vent sites relative to others with which they are directly linked, revealing three qualitative network clusters.
These three network clusters are the vent sites of the Mariana Trough, the Okinawa Trough, and the Izu-Bonin-Mariana Arc. The betweenness centrality of nodes (Table 1) was high for those vent sites that linked the three network clusters: Northwest Eifuku, Forecast, and Myojin Knoll. However, the SIMPROF analysis returned 11 groups (SIMPROF clusters) of vent sites each of which has no significant structural differences among their species assemblages ( Figure 3). of variation on its own and a further 15% as an interaction with the broad-scale dbMEM. Depth was checked for spatial autocorrelation following the methods outlined in Legendre and Legendre (2012) and found to be nonsignificant.

| Bipartite network
The simulated annealing method (Guimerà & Nunes Amaral, 2005a, 2005b) detected five distinct modules ( Figure 5). Hereafter, we call The distribution of vent sites in terms of within-module degree (z i ) and participation coefficient (P i ), as well as their category of "universal cartographic roles" (Guimerà & Nunes Amaral, 2005b), is presented in Figure 6. The universal cartographic roles of species nodes are summarized in

| DISCUSS ION
The results obtained from the two networks generated in this study generally agree on the regional diversity structure and the significance of the environmental drivers we investigated. Although both networks are formed from the same species assemblage data, they each have distinct advantages in the way they can be interpreted.
The similarity network builds upon common ecological analyses but also extends traditional clustering approaches to detect sites that act as intermediaries between clusters. Furthermore, displaying βdiversity as a network of similarity edges is arguably a much more intuitive way of visualizing species diversity and inferred connectivity. The bipartite network is less intuitive in its presentation, but it can go beyond the detection of intermediary sites between clusters F I G U R E 2 Similarity network of the Northwest Pacific vents showing qualitative clustering based on tectonic basin. The shape represents the tectonic setting of each vent site, and the color represents their basin. Edges represent pairwise similarity values among vent nodes above the percolation threshold. The relative position of the vent nodes is dictated by the shared edges.

F I G U R E 4
Variance partitioning of vent site dissimilarity (Sorensen's coefficient) against local environmental variables (depth and tectonic setting), fine-scale dbMEM, and broad-scale dbMEM. The low residuals show how well these variables predict dissimilarity, particularly the broad-scale dbMEM.
and identify sites that act as hubs of shared species within their cluster (module). Previous studies have used both network methods to study other ecosystems at various scales but not in combination.
The discrete nature and dispersal processes that dictate connectivity at hydrothermal vents make them particularly suitable for the application of such network methods.

| Regional diversity structure
At the scale of the entire region, the structure of the similarity network has "small world" properties due to the presence of tight clusters of nodes with key connectivity pathways ( Figure 2) between them (Watts & Strogatz, 1998). This same small world Table S3. While no species are shared among the three sub-regions, IBMa's role as an intermediary between OT and MT is highlighted by its shared species.

F I G U R E 6
Within-module degree and participation coefficient of each vent site in the bipartite network. Colored by module membership as in Figure 5; purple-OT, orange-ME, red-Su, green-IBMa, blue-MT.
structure can be seen in the vent larvae dispersal network of The clustering that gives the similarity network its small world properties is evident in the spatial arrangement of nodes based on their shared edges ( Figure 2) and the high betweenness centrality of the key nodes ( Table 1)  Okinawa Trough, respectively, in the present study (Figure 7). The occurrence of Sumisu Caldera as an outlier can be explained, not by its shallow depth, but by its similarity in community composition to cold seeps (Nakajima et al., 2014) likely due to its weak venting activity (Iwabuchi, 1999). The Minami-Ensei Knoll community, on the other hand, does not resemble a cold seep in its community composition but does have a large proportion of endemic species and a few species uniquely shared with IBMa ( Figure 5).

Minami-Ensei
Knoll may be distinct within the Okinawa Trough because of its shallow depth (Figure 7) or high methane concentrations relative to the other vent sites in this area (recorded by Chiba (1993) and compared by Nakajima et al. (2014)).
There are minor discrepancies in group membership between the different methods. We prefer the results from the bipartite modularity analysis due to its objectivity compared with similarity network clusters and the additional insights it provides compared with the SIMPROF clusters (Bloomfield et al., 2018). Furthermore, the significant difference of depth, tectonic setting, and broad-scale dbMEM among the five modules from the MANOVA analysis validates the groups detected by the bipartite modularity analysis, which was not given information on these parameters a priori. Northwest Eifuku (NrtE), due to its connection to Forecast (Frcs) ( Figure 2). The participation coefficient suggests that Northwest Rota-1 volcano (NRv) is a more important connection between subregions ( Figure 6). This discrepancy is likely due to the centrality measure ignoring "weak" connections removed by the thresholding step. The "strong" connection between NrtE and Frcs may be due to their depth proximity (Figure 7), while the weak connections of NRv and vent sites in the Mariana Trough may be driven by their geographic proximity (Figure 1). Previous studies have suggested that nodes with high centrality in vent similarity networks represent historical stepping-stones between biogeographic provinces (Kiel, 2016;Moalic et al., 2012). Such historical factors can have a strong role in present-day biogeography at vents (Kiel, 2017).
Although we were able to detect vent sites that act as important intermediaries among sub-regions using the similarity network ( Figure 2), the bipartite network was not able to detect equivalent "connector nodes" based on Guimerà and Nunes Amaral's (2005a) universal cartographic roles ( Figure 6). The lack of vent site connector nodes and the prevalence of periphery nodes in the regional bipartite network is unsurprising considering the large number of module endemics ( Figure 5). No species are present in all five modules or even all three sub-regions. Several species are present in three modules (two sub-regions and an outlier site). Of particular note is the species Gandalfus yunohana (Takeda et al., 2000) (node 28, Figure 5), which is classified as a Network Hub due to its presence in multiple vent sites of OT and IBMa as well as Sumisu Caldera.
Additionally, Enigmaticolus nipponensis (Okutani & Fujikura, 2000) and Alvinocaris brevitelsonis (Kikuchi & Hashimoto, 2000) (nodes 38 and 76, Figure 5) are classified as connector nodes because of their presence in multiple vent sites of OT and IBMa as well as Minami-Ensei Knoll. These species may have functional traits that allow them to cross these biogeographic barriers through strong dispersal or colonization ability (Economo et al., 2015).

| Intra-module diversity structure
Only the bipartite modularity methods were able to detect the relative importance of vent sites in driving within-group diversity structure ( Figure 6). Based on the shared species as well as geographic and environmental proximity, the sub-regions may represent three distinct metacommunities of hydrothermal vent species in the region. Regardless of whether they are distinct metacommunities, biogeographic sub-regions, or even regions, each identified module should be treated as an independent biodiversity management unit (Borthagaray et al., 2018). Thus, the vent sites most important for maintaining network connectivity within their respective modules (highest z i score) should be protected to preserve regional diversity because they may also be key to maintaining gene flow within a metacommunity context. In this context, Sakai (Saka), Alice Springs Field However, the central Okinawa Trough is confirmed as an area of interest for planned hydrothermal vent mining (Okamoto et al., 2019), although specific targets for mining remain publicly unconfirmed.
However, the massive sulfide deposits in the Okinawa Trough at Sakai, Izena Cauldron, and Daisan-Kume Knoll are of particular interest (Ishibashi et al., 2015;Minami & Ohara, 2017). As a "module hub" for the OT sub-region with the highest within-module degree, any disturbance to Sakai from mining activity will have a disproportionately strong impact on the linkages among vent sites within the Okinawa Trough metacommunity. The high betweenness centrality of Daisan-Kume Knoll suggests it plays an important role in sharing species between the Okinawa Trough and Izu-Bonin Arc; this may be due to its tectonic features that make it an intermediary in terms of local environmental conditions.

| Drivers of the diversity structures
The multivariate statistics applied to investigate the drivers of diversity in this study found that the explanatory variables used (depth, tectonic setting, and geodesic distance) explained much of the variation in species distribution data among vent sites ( Figure 4) and varied significantly among modules. However, there are likely several other abiotic, biotic and historical covariates that drive regional diversity at the biogeographic and metacommunity scale. The low betweensite variation explained by the environment (depth and tectonic setting) alone is likely due to unknown covariates. Tectonic setting, for example, can influence various parameters such as venting fluid composition, intensity, and stability (Gamo et al., 2013;Mullineaux et al., 2018), which can be drivers of community composition at vents (Juniper & Tunnicliffe, 1997;. Furthermore, dissimilarities in venting fluid composition in similar tectonic settings of distinct hydrothermal regions occur because of differences in the local sediment layers, material supply from the subducting slab, and boiling points (Gamo et al., 2013). Although the vent-obligate species of this study are mostly independent of surface-derived food, depth is associated with vent community structuring in both this study and previous studies in the Northwest Pacific Nakajima et al., 2014;Watanabe et al., 2019;. Important local controls on community composition can co-vary with depth, for example, the Izu-Bonin Arc, vent sites that occur on a volcanic arc are characteristically shallower than those that occur on the corresponding backarc spreading centre (Figure 7). In general, emissions from vents on volcanic arcs are more acidic and regionally variable than those from back-arc settings (Resing et al., 2009).
The two dbMEMs explain most of the species assemblage variation and represent their relative geographical position. The calculation of the fine-scale dbMEM assumes that vent sites in the Okinawa Trough are disconnected from the rest of the regional network while the broad-scale dbMEM assumes at least indirect connectivity among all vent sites in the Northwest Pacific. It is therefore unsurprising that the broad-scale dbMEM explains more variation among sites (22% compared with 7%) as it takes into account the species turnover among vent sites of the entire region. Connectivity among vent sites in the Northwest Pacific via larval dispersal is theoretically possible under certain assumptions of larval dispersal behavior (Breusing et al., 2021;Mitarai et al., 2016). This study also demonstrated that dispersal probability was variable among vent sites of the same subregional modules. It is possible that variable dispersal probabilities among vent sites in the Northwest Pacific are responsible for the variation explained by the fine-and broad-scale dbMEM variables.
However, both dbMEMs also encompass all spatially autocorrelated variables that we were not able to account for in our analyses (Peres-Neto & Legendre, 2010). It is therefore likely that the variation explained by these spatial variables represents a dispersal limitation as well as spatially autocorrelated abiotic responses and biotic interactions among species (Thompson et al., 2020). Furthermore, the nature of the dbMEMs assumes that vent sites of a certain geodesic distance from one another can share species and does not take into account the isolation that could be caused by oceanographic or topographic features in the region (Mitarai et al., 2016;Watanabe et al., 2019).
With additional data on relative abundances and/or time series, it would be possible to distinguish the relative roles that biotic, abiotic, and dispersal filters play in structuring the metacommunity (Guzman et al., 2022;Thompson et al., 2020). The uncertainty originating from the multivariate analyses and the lack of available local biotic and abiotic parameters-a common limitation for such remote ecosystems-precludes us from pinpointing the drivers of regional diversity and we simply assume that these critical metacommunity processes are driving the distribution of species. However, the significance of the parameters tested in combination with the structural aspects of the networks allowed us to explore hypotheses of diversity drivers at hydrothermal vents, which could be tested in the future via targeted collections of additional data.

| CON CLUS ION
Based on our analyses of species distribution data, we suggest that connectivity among the three sub-regions, as they are key nodes in preventing the collapse of the regional network (Thompson et al., 2015). The vent sites most important for linking within the subregions of the Okinawa Trough, Izu-Bonin-Mariana Arc, and Mariana Back-arc are Sakai, Nikko Volcano, and Alice Springs Field, respectively. These three vent sites play the most important role in maintaining biodiversity in the Northwest Pacific, on time scales pertinent to conservation, by connecting the sub-regional metacommunities through shared species. Additional biological data are required to disentangle the relative roles of local environmental filtering, biotic interactions, and dispersal in structuring the metacommunity at the regional scale. However, the network approach of this study enables us to indicate key vent sites for maintaining regional diversity based on species distribution data; this is a powerful tool for informing the conservation of this remote and vulnerable ecosystem through spatial management strategies in the age of deep-sea mining.

ACK N OWLED G M ENTS
We greatly appreciate the tireless work of all captains, crews, and scientists on-board the many expeditions involving multiple sub-

DATA AVA I L A B I L I T Y S TAT E M E N T
All data used in this study are available within the supplementary tables in the form of excel spreadsheets.